





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a   	=    logging.get_logger(__name__)

a   	=    {
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}







class __a			( a_      ):
   __UpperCamelCase :			List[Any]						   =						'nllb-moe'
   __UpperCamelCase :			Any						   =						['past_key_values']
   __UpperCamelCase :			Optional[int]						   =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      Dict=12_8112					,lowerCamelCase					:      List[str]=1024					,lowerCamelCase					:      int=12					,lowerCamelCase					:      Any=4096					,lowerCamelCase					:      List[str]=16					,lowerCamelCase					:      List[Any]=12					,lowerCamelCase					:      List[Any]=4096					,lowerCamelCase					:      Any=16					,lowerCamelCase					:      Optional[Any]=0.05					,lowerCamelCase					:      List[Any]=0.05					,lowerCamelCase					:      Tuple=True					,lowerCamelCase					:      List[Any]=True					,lowerCamelCase					:      Tuple="relu"					,lowerCamelCase					:      Union[str, Any]=1024					,lowerCamelCase					:      Optional[int]=0.1					,lowerCamelCase					:      str=0.1					,lowerCamelCase					:      int=0.0					,lowerCamelCase					:      Dict=0.02					,lowerCamelCase					:      str=2					,lowerCamelCase					:      Any=True					,lowerCamelCase					:      str=False					,lowerCamelCase					:      List[Any]="float32"					,lowerCamelCase					:      Any=False					,lowerCamelCase					:      int=128					,lowerCamelCase					:      Union[str, Any]=64					,lowerCamelCase					:      int=4					,lowerCamelCase					:      str=4					,lowerCamelCase					:      str=0.001					,lowerCamelCase					:      Any=0.001					,lowerCamelCase					:      List[Any]="all"					,lowerCamelCase					:      List[Any]=False					,lowerCamelCase					:      Tuple=False					,lowerCamelCase					:      Union[str, Any]=1.0					,lowerCamelCase					:      List[Any]=0.2					,lowerCamelCase					:      Optional[int]=1					,lowerCamelCase					:      Optional[int]=0					,lowerCamelCase					:      List[Any]=2					,lowerCamelCase					:      List[str]=False					,**lowerCamelCase					:      List[Any]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							max_position_embeddings
      __SCREAMING_SNAKE_CASE   				=							d_model
      __SCREAMING_SNAKE_CASE   				=							encoder_ffn_dim
      __SCREAMING_SNAKE_CASE   				=							encoder_layers
      __SCREAMING_SNAKE_CASE   				=							encoder_attention_heads
      __SCREAMING_SNAKE_CASE   				=							decoder_ffn_dim
      __SCREAMING_SNAKE_CASE   				=							decoder_layers
      __SCREAMING_SNAKE_CASE   				=							decoder_attention_heads
      __SCREAMING_SNAKE_CASE   				=							dropout
      __SCREAMING_SNAKE_CASE   				=							attention_dropout
      __SCREAMING_SNAKE_CASE   				=							activation_dropout
      __SCREAMING_SNAKE_CASE   				=							activation_function
      __SCREAMING_SNAKE_CASE   				=							init_std
      __SCREAMING_SNAKE_CASE   				=							encoder_layerdrop
      __SCREAMING_SNAKE_CASE   				=							decoder_layerdrop
      __SCREAMING_SNAKE_CASE   				=							use_cache
      __SCREAMING_SNAKE_CASE   				=							encoder_layers
      __SCREAMING_SNAKE_CASE   				=							scale_embedding  # scale factor will be sqrt(d_model) if True
      __SCREAMING_SNAKE_CASE   				=							router_z_loss_coef
      __SCREAMING_SNAKE_CASE   				=							router_aux_loss_coef
      __SCREAMING_SNAKE_CASE   				=							decoder_sparse_step
      __SCREAMING_SNAKE_CASE   				=							encoder_sparse_step
      __SCREAMING_SNAKE_CASE   				=							num_experts
      __SCREAMING_SNAKE_CASE   				=							expert_capacity
      __SCREAMING_SNAKE_CASE   				=							router_bias
      if router_dtype not in ["float32", "float16", "bfloat16"]:
         raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""     )
      __SCREAMING_SNAKE_CASE   				=							router_dtype

      __SCREAMING_SNAKE_CASE   				=							router_ignore_padding_tokens
      __SCREAMING_SNAKE_CASE   				=							batch_prioritized_routing
      __SCREAMING_SNAKE_CASE   				=							second_expert_policy
      __SCREAMING_SNAKE_CASE   				=							normalize_router_prob_before_dropping
      __SCREAMING_SNAKE_CASE   				=							moe_eval_capacity_token_fraction
      __SCREAMING_SNAKE_CASE   				=							moe_token_dropout
      __SCREAMING_SNAKE_CASE   				=							output_router_logits
      super().__init__(
          pad_token_id=UpperCamelCase_					,bos_token_id=UpperCamelCase_					,eos_token_id=UpperCamelCase_					,is_encoder_decoder=UpperCamelCase_					,decoder_start_token_id=UpperCamelCase_					,**UpperCamelCase_					,)













"""simple docstring"""
def 							A						(				__snake_case: str  )    ->		list:

       """simple docstring"""





       return [
           txt[:a] + txt[a].upper() + txt[a + 1 :]
           for a in range(len(__snake_case  )  )
           if txt[a].isalpha()
       ]


if __name__ == "__main__":
 __import__("""doctest""").testmod()